Systems and methods for predicting weather performance for a vehicle

ABSTRACT

Systems and methods for obtaining data about road conditions as they pertain to an individual vehicle, using this information to build a model of vehicle behavior as a function of its environment, and aggregating information concerning multiple vehicles along with data from other sources in order to predict vehicle behavior in future environments.

CROSS REFERENCE

This application is a continuation application of U.S. patentapplication Ser. No. 14/516,309, filed Oct. 16, 2014, and claims thebenefit of the filing date of U.S. Provisional Application No.61/892,166 having a filing date of Oct. 17, 2013, the entire contents ofboth applications are incorporated by reference in their entirety asthough set forth herein.

FIELD OF THE INVENTION

The present invention relates to a system including a set of sensorscapable of collecting information on the environment of a vehicle-groundinterface, and methods for the use of this information to improvevehicle safety.

BACKGROUND OF THE INVENTION

Environmental conditions significantly impact vehicle behavior. This ismost commonly noted as degradation of vehicle stopping capabilities ininclement weather such as snow or ice. Such degradations mean thatdriver behavior should ideally adapt to match immediate road conditions,and that in some cases drivers should entirely avoid areas deemed to betoo dangerous, for example those with “black ice”.

Road conditions can be generally estimated based on known weatherconditions. However, both weather conditions and road temperatures canvary dramatically over short distances, so that general area weatherforecasts are insufficient to provide specific driving advice to avehicle in a particular area. Thus, more granular data on weather, andspecifically on road conditions, would be of value to improve the safetyof drivers.

Stopping distance and general vehicle safety also depends dramaticallyon the specific vehicle being driven. Vehicle stopping distances mayvary based on vehicle model, vehicle weight, brake quality, and tiretread conditions. Thus, in a defined road location two vehicles withdifferent characteristics may experience dramatically different stoppingdistances. As a result, knowledge of the weather or road conditionsthemselves are not sufficient to ensure driver safety.

The interplay between vehicle and road at a given instant is consideredas an input in existing anti-lock braking systems (ABS). In suchsystems, the tangential acceleration of one or more wheels is measuredand compared with the acceleration rate of the vehicle. Because the tirehas lower mass than the vehicle it can decelerate much more quickly thanthe vehicle, and as a result the tire can “lock” in a state where itdoes not rotate. This locking is undesirable, because the co-efficientof friction of a tire in its locked state is substantially lower thanthe optimal coefficient of friction for that tire.

ABS uses a closed-loop control process to optimize the amount ofrotation in the tire, and thus optimize coefficient of friction. In ABS,the amount of force applied to the brakes is automatically relaxed iflock (or, more generally, slip) is detected in order to allow the tireto rotate again. The braking is re-established once rotation is sensed.Ideally, this system functions so that the optimum coefficient offriction (corresponding to an optimum amount of tire slip) is maintainedduring braking.

While the above closed-loop system can provide excellent control overvehicle braking in an emergency situation, it is not capable of makingpredictions of future vehicle safety performance, or of assessing itsperformance versus a baseline. While ABS assures “optimum” braking forthe particular emergency case, there is no ability to analyze whetherthis “optimum” is good enough—whether it represents a safety performancelevel that will be satisfactory in other situations.

Thus, while it is possible to provide general advice for a genericvehicle during inclement weather, and it is possible to optimize thesafety of a specific vehicle after a loss of control has occurred, it isnot currently possible to provide targeted advice to a vehicle about howwell it can perform in specific weather conditions and/or upcoming roadconditions.

SUMMARY OF THE INVENTION

The presented inventions are directed to a system that includes sensorsand sensor systems, and methods for analyzing data from these sensors,in order to measure characteristics of the tire/road interface invarying environmental conditions, as well as to provide information,guidance, and predictions to drivers, fleet managers, traffic managers,safety services, navigation and/or self-driving vehicle systems, models,services and other interested parties that use weather information andpredictions.

One aspect of the invention is the fusion of multiple sensors, sourcesof information (including databases) along with models to create orprovide information that is not available through the individual sensorsalone. In effect, many sensors are actually effected through sensorfusion—for example, differential GPS is effected using the outputs ofmore than one GPS sensor. If the sensors are not convenientlyco-located, the communication system(s) become an integral part of thesensor fusion. In many sensor fusion applications, processing power andmodelling are also an integral part of the senor fusion. Data used inthe model(s)—say data from a database—may become, in essence, anothersensor that is fused. An example here would be the street map databasefor a Geographic information system (GIS) being fused with GPSinformation to display a real time position map on a smartphone of amoving cars position.

In one embodiment, sensor fusion is done before transmission to reducethe use of limited bandwidth, and to reduce the costs associated withusing costly bandwidth (e.g. cell connections). In another embodiment,models reside on the sensor or hubs processor to reduce the costs orbandwidth of transmission, and these models may be updated using OTA.

In some embodiments, the presented inventions includes inertialmeasurement sensors comprising accelerometer(s), gyroscope(s), and/ormagnetometer(s) that are added to a vehicle to measure its motion. Insome embodiments, these inertial measurement sensors are added directlyto one or more wheels of the vehicle to measure its tangential motion,velocity, and/or acceleration. One or more such sensors may be added toa non-rotating member of the vehicle such as the bumper to measure itslinear and angular motion and or position. In some embodiments, thesensors are added at the lug nut of the wheel to capture tangentialacceleration at this position. In other embodiments, one or more sensorsare added to a tire pressure monitoring system (TPMS), or affixed insideor outside of the tire or axel.

In one aspect of the presented inventions, sensor data is used tocompute coefficients of friction and slip ratios for the vehicle incertain situations. For example, the wheel rotational accelerationand/or velocity are compared with the linear vehicle acceleration and/orvelocity and the difference between the two are computed in order toprovide an estimate of coefficient of friction and/or slip ratio.Multiple such measurements may be utilized to generate curves, equationsand/or tables of coefficient of friction vs. slip ratios. Likewise, suchcurves, equations and/or tables may be generated for differingenvironmental and/or road conditions. In another example, the change invelocity and/or acceleration of a vehicle is calculated during a brakingsituation in order to provide an estimate of coefficient of friction. Insome embodiments, this rate of change is measured by using GPS toidentify the known distance over which braking has occurred, andmeasurement of the total time of braking in order to establish the timeover which braking has occurred. In some embodiments, the wheelrotational orientation and vehicle speed along a road with a knowngeometry is measured in order to estimate the weight of the vehicle. Insome embodiments, sensors such as radar, lidar, sonar, or (e.g. 3D)computer vision are used to measure/estimate the distance to otherobjects, which can be combined with stopping distance information toprovide safety information. In some embodiment, computer vision is usedto determine visibility, weather conditions (e.g. sleet hail, or blackice), road conditions (e.g. potholes and buckling) and roadside hazardsand issues (e.g. semi-tractor trailer tires that have been shed, deadanimal etc.). In other embodiments, the tire pressure is measured inorder to estimate the vehicle's tire radius and/or contact surface withthe ground.

In another aspect of the presented inventions, profiles of coefficientsof frictions and slip ratio and plots of coefficient of friction (COF)versus slip ratio for a vehicle are compiled over time, across a varietyof road environments. In one embodiment, these profiles are tagged withinformation about geographic position and/or are tagged with informationabout time. In one embodiment, these profiles are tagged withinformation about environmental conditions. Such environmentalconditions may be identified from information provided from the NationalWeather Service or National Center for Atmospheric Research's (NCAR's)Pikalert system, Road Weather Information System (RWIS), MeteorologicalTerminal Aviation Routine Weather Report (METAR) or Terminal AerodromeForecast (TAF), UCAR's Location Data Manager (LDM) Etc. In anotherembodiment, the environment conditions are derived, at least in part, bysensor(s) on or near a vehicle at the time the measurements relating tocoefficient of friction and slip ratio are taken. In one embodiment,local precipitation is measured using a precipitation gauge mounted onthe vehicle, for example on the front windshield. In such an embodiment,the type of precipitation (e.g., rain, snow) is measured directly by theprecipitation sensor or inferred from a combination of sensormeasurements. In one embodiment, local road temperature and conditionsare monitored by an infrared camera mounted to the vehicle, for exampleon the vehicle bumper. Likewise, light or camera sensors may be used todetect/measure cloud cover. Further, motion sensors may be used todetect/measure wind velocity and gusts.

In one embodiment, at least one of these sensors communicates to a hubdevice using a wireless communications protocol. In one embodiment, thiswireless communications protocol is Bluetooth or Bluetooth Low Energy.In one embodiment, this wireless communication uses a technique otherthan conventional electromagnetic radiation, such as magnetic oracoustic communication. In one embodiment, the hub device is a cellularphone. In one embodiment, this cellular phone has one or more applicablesensors, such as an Inertial Measurement Unit. In one embodiment, thehub is connected to the On Board Diagnostics (OBD) system of the car,drawing power and/or measurements from the OBD. In one embodiment, thehub is a device capable of running many applications that make use ofthe systems capabilities (e.g., an Android device).

In still another aspect of the presented inventions, the COF or COF vsslip ratio curve for a vehicle are predicted for future environmentalconditions and/or future road conditions based on the past COFperformance of the vehicle. In one embodiment, the future environmentalcondition is chosen based on a vehicle's expected travel path. In oneembodiment, the future environmental condition represents the presentenvironmental condition at a location that the vehicle will soon be in.In one embodiment, the future environmental condition includes aprediction of the environmental state of that location based on acombination of the present environmental condition and a model thatpredicts environmental changes. In one embodiment, the futureenvironmental condition is derived at least in part from a report fromthe National Weather Service. In one embodiment, the futureenvironmental condition is derived at least in part from environmentaldata taken at that location by fixed sensors. In one embodiment, thefuture environmental condition is derived at least in part fromenvironmental data taken at that location by mobile sensors. In oneembodiment the mobile sensors are affixed to other vehicles. In anotherembodiment, future road conditions are derived at least in part fromroad condition information taken by mobile sensors. In one particularembodiment, future or upcoming coefficient of friction informationand/or environmental information for a travel path of a vehicle areprovided to the vehicle. This upcoming road surface information may beutilized with stored profile information of the vehicle to determinevehicle specific safety information and/or to generate warning outputs.

In yet another aspect of the invention, the future COF is obtained bymatching the previously measured COF values and/or curves withenvironments that resemble the future environment, and selecting COFvalues that most closely match that environment. In one embodiment, thefuture COF is obtained by first building a model for COF as a functionof environmental conditions for a particular vehicle, and thenextrapolating from this model to predict the COF for these futureenvironmental conditions. In one embodiment of the invention, data fromone or more sensors, vehicles, etc., is stored in a computer database.In another embodiment, models are constructed using Big Data (dataanalytics/predictive analytics) methods and/or control theory methodssuch as system identification.

In further aspects of the invention, COF and COF versus slip ratio datafor a plurality of vehicles are compiled to form a library of COF data.In one embodiment, data from more than one vehicle in this library iscombined to form at least one element of an assessment of roadconditions in a specific location common to these vehicles. In oneembodiment, the future COF of a first vehicle is predicted based on amathematical model which comprises data from vehicles other than thisfirst vehicle.

In still yet another aspect of the invention, the driver, owner,insurer, or other interested party of a vehicle are alerted to thepotential for poor safety performance at a future time. In oneembodiment, the interested party is notified if the vehicle's futurepath is anticipated to take the vehicle to a location where itspredicted COF will be below a threshold level. In another embodiment,the interested party is notified if the COF is predicted to fall below athreshold value in weather conditions that are common to the vehiclelocation. These alerts may be output in any appropriate manner to adriver of the vehicle and/or to vehicle systems (e.g., tractioncontrol).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a relationship between COF and slip ratio.

FIG. 2 shows an illustration of the forces impinging on a block slidingon an inclined plane.

FIG. 3 shows a perspective view of a vehicle with sensors formed inaccordance with embodiments of the invention.

FIG. 4 shows a block diagram of communication and processing systemsformed in accordance with various embodiments of the invention.

FIG. 5 shows an exploded view of a lug nut sensor formed in accordancewith one embodiment of the invention.

FIG. 6 shows an exploded view of a wheel-mounted sensor at or near thetire pressure measurement system formed in accordance with oneembodiment of the invention.

FIG. 7 shows an exploded view of a bumper sensor suite formed inaccordance with one embodiment of the invention.

FIG. 8 shows an exploded view of a windshield sensor formed inaccordance with one embodiment of the invention.

FIG. 9 shows an exemplary system that forms a COF/slip profile fromsensor data.

FIG. 10 shows an exemplary system that estimates current environmentalconditions from sensor data.

FIG. 11 shows an exemplary system that notifies an interested party inthe vehicle if the vehicle is anticipated to encounter a potentiallyhazardous environment.

FIG. 12 shows an exemplary system that combines multiple vehicle outputsand/or profiles into a library which can be used to predict vehicleperformance in future environments.

FIG. 13 shows exemplary COF/slip profiles for a vehicle for differentenvironmental/road conditions.

FIG. 14 shows a travel path of a vehicle with road surface informationfor different road segments of the travel path.

FIG. 15 shows an alternate suggested route for the travel path of FIG.14.

FIG. 16 shows a process for generating safety outputs at a vehicle.

FIG. 17 shows a process for gathering and distributing road surfaceinformation.

DESCRIPTION OF THE INVENTION

The present invention identifies new and surprising methods forimproving vehicle safety by enabling prediction of coefficient offriction, slip ratio, and/or stopping distance for a specific vehicle ata time in the future. The present invention includes sensor(s) andcomponents that perform analysis techniques to customize a prediction toa particular vehicle in order to optimize the utility of theinformation.

Generally, aspects of the presented inventions use techniques of“information fusion” to create new information. A definition ofinformation fusion is provided by the International Society ofInformation Fusion: “Information fusion is the study of efficientmethods for automatically or semi-automatically transforming informationfrom different sources and different points in time into arepresentation that provides effective support for human or automateddecision making” These different sources can include at least twoelements from the classes comprising sensors, external data sources,mathematical models, algorithms, etc., as well as combinations of theseelements that may be generically described as sensors in thesedescriptions

Information fusion can be used to combine measurements/data (andinformation) from more than one source—often in concert with models inways that allow one to access information and make predictions aboutquantities and qualities using sensor fusion and/or information fusionthat are not present in any raw measurements/data from one source. Amodel in this context represents a mathematical representation of aphysical system, wherein, for example, the physics of one characteristiccan be estimated or predicted based on input values from othercharacteristics, and includes a broad range of techniques such firstprinciple dynamic models, statistical models, system identification, andneural network and deep learning systems. Data generated by such aprocess can be broadly called “fusion data”, and may be used directly orserve as an input to another model.

This analysis can result in an estimate of a characteristic of a systemthat is not directly measured by the sensors. Additionally, one can fusetwo measurements of similar quantities into improved information, forexample using two measurements—one accurate not precise, and one precisebut not accurate—into an estimate of the quantity which is both moreaccurate and precise.

Sensors and information sources which have applications to vehiclesensor/information fusion and or safety include: vehicle sensorsnetworked to the On Board Diagnostics (OBD, dashboard cameras (includingdual, three-dimensional, and array cameras, and rearview/backup and/or360 view cameras, as well as driver and passenger facing cameras),spectroscopic sensor systems, visibility sensors (e.g. extinctioncoefficient backscattering sensors or integrating nephelometer), sensorssuch as magnetic loops, micro radar, temperature, and magneto-restivewired and wireless sensors (which may or may not be embedded in thepavement), toll-taking sensors (including RFID, DSRC, and othertechnologies), distrometers, particulate counters, and ceilometers,lightning sensors, linear optical arrays, proximity detectors, magneticposition sensors, gas sensors, color sensors, infrared pyrometers(especially in linear arrays) and cameras (e.g. temperature sensors),RFID and other location tagging, blind spot sensors such as radar, carahead-behind distance sensors, and pavement sensors optical and spectralanalysis sensors, battery “fuel gauges”, infrared pyrometers, andlocation technologies such as GPS, Galileo, and Glonass, as well asintegrated systems such as GNSS, sensors for precipitation type andamounts, wiper sensor, irradiance and UV/IR sensors (useful both forweather measurements and as instrumentation for estimating availablePhotovoltaic energy), cloud sensors, roadside snow sensors, computervision (e.g. sensing lane markers, other vehicles, and roadside trafficmarkers), lightning sensors, barometric pressure, particulate count, andpollution and chemical sensors, sonic sensors and microphones (e.g.sensors for use in creating sonic profiles of road and tier noise and/ordoing FFT analysis of sounds, for the purposes of sensing road surfacetype, road surface conditions, and precipitation-tire interaction),range finding sensors (e.g. distance to vehicle in front/back), memssensors, etc. Sensors may also be in the form of information fromvehicles and vehicle management systems, such as traffic jam auto drive,auto park, parking space management, and GIS systems with mutli-layerdata sets about vehicles, conditions, weather, predictive analytics,etc. Sensors may be the outputs of smart phone and/orcar sensorsets—connected to internet via cell, wifi, Bluetooth, etc. Smart phonesthemselves make excellent information fusion devices, containing agrowing number of sensors and communications methods, as well asever-increasing processing power and access to algorithms and databasesvia the internet.

These sensors can, as is appropriate, be connected sensors on vehicles,infrastructure, or persons, be part of connected technologies such assmart phones, smart watches, personal computers, vehicleinstrumentation, be part of safety systems such as National WeatherService warning systems, police and fire response, traffic accidentreports and lane closure warnings, General Motors OnStar system, etc.Sensors may also be in the form of crowd-sourced information, databasesinformation, broadcasts, etc.

Sensors, information databases, models, processing power (includingcloud technologies), and other elements of information fusion are nowoften distributed, and thus communications between these elements may becritical. Cell communications have become ubiquitous methods ofcommunication, and have become well integrated in vehicleapplications—standards include GSM (Global System for MobileCommunications, a de facto global standard for mobile communicationsthat has expanded over time to include data communications. Otherstandards include third generation (3G) UMTS standards and fourthgeneration (4G) LTE Advanced standards. Additional communication methodsapplicable for the invention include: Satellite internet and telephony,Bluetooth and Bluetooth low energy, wifi, using regulated andunregulated frequency's (such as ISM), whitespace, DSRC (Dedicated ShortRange Communications) including but not limited to vehicle to vehicle(including ad hoc networking for passing info such as braking, swerving,GPS position and velocities for avoidance, or in our case COF-Slip) andvehicle to infrastructure (such as traffic signals and signs), radio,repeaters, VHS (such as aircraft bands), infrared, spread spectrum,mobile ad hoc networks (MANETs) and mesh networks, public informationsystems such as the 5-1-1 telephone car information system (road weatherinformation, ans transportation and traffic information telephonehotline) and National Weather Service Emergency Broadcast systems, andpolice, fire, ambulance, and rescue bands and systems. This list (andothers in this specification) are to be considered illustrative and inno way limiting.

In one embodiment, input to a model takes the form of a specificmeasurement (e.g., a value). Input to a model can also take the form ofa relationship between two variables, which together define a curve. Anexample value is the instantaneously measured wheel slip ratio of avehicle. An example curve is a plot of a relationship between wheel slipratio and COF for a specific environmental condition. A curve includesmeasured data and/or data extrapolated from models.

In one embodiment, input to a model takes the form a more complex“profile”, which includes an array of data associated with a givenvehicle. An example profile in this invention is an array of COF versusslip ratio curves for a single vehicle in a number of differentenvironmental conditions. In one embodiment, a profile includes measureddata as well as data extrapolated from models. In one embodiment, thisprofile(s) is stored at the vehicle in a memory unit. In anotherembodiment, this profile(s) is stored remotely from the vehicle and isaccessible by the vehicle and/or similar vehicles via a communicationsmodule. In the latter regard, vehicles that have not yet calculated aprofile(s) or dot not have adequate sensors to calculate such aprofile(s) can access pertinent profile information.

In one embodiment, input to a model takes the form of a “library”. Alibrary includes a set of multiple profiles. For example, such a librarymay include COF data from all vehicles that have passed by a particularlocation and/or includes COF data from a set of similar vehicles, orvehicles with similar tires, or of similar ages, etc. A library is usedfor inferring the anticipated characteristics of a specific vehicle bycomparison with other, similar vehicles. A library includes measureddata as well as data extrapolated from models.

Output from the model is termed a “prediction”, and represents anestimate of the current or future state of a variable that is notdirectly measured by the sensors. An example prediction from a model isthe maximum COF of a vehicle in environmental conditions in a regionbeyond the exact region of the vehicle at a particular moment. A“vehicle” in this invention can refer to one or more commonly usedtransportation systems, including a car, a self-driving car or drone,truck, etc.

One metric of the safety of a vehicle is the vehicle stopping distances.The stopping distance is determined by several factors, including thespeed of the vehicle, the mass of the vehicle, and the coefficient offriction between the vehicle and the road. While the vehicle mass can bereasonably estimated by the driver, and its speed is constantly measuredby the speedometer, the coefficient of friction is usually not known tothe driver, as it is not measured or reported by vehicle systems. Thecoefficient of friction represents the most significant uncontrolledvariable in vehicle safety. Worse, the coefficient of friction canchange suddenly on a road, for example as a vehicle moves from dry roadto a puddle, or from snowpack to black ice. As a result, road safety isbest quantified through coefficient of friction, and means to bothmeasure and predict coefficient of friction produce valuable safetyimprovements.

The coefficient of friction at the tire/road interface varies as afunction of the “slip ratio” of the wheel, where a slip ratio of zeroindicates a freely rolling tire, and a slip ratio of one indicates acompletely locked tire. Without being bound by theory, it is believedthat the coefficient of friction between tire and road has a maximum ata specific slip ratio. FIG. 1 shows a typical relationship betweencoefficient of friction and slip ratio. A relationship such as this isreferred to as a “COF curve” for a specific road condition. Anti-lockbrakes attempt to maintain the COF as close as possible to the maximumvalue of this curve during aggressive braking. A locked wheel hassignificantly lower COF than the maximum achievable COF, and istherefore to be avoided if possible. The exact details of this curve,including the COF maximum value, depend on the specifics of the tire,vehicle velocity, and the environmental conditions of the road (e.g.,clean dry asphalt, dirt road, packed snow on concrete, etc.).

In one embodiment, the present invention produces one or more profilesof COF values and COF curves associated with a particular vehicle. See,for example, FIG. 13. The profiles are created by measuring COF and/orslip ratio using at least one sensor under at least two environmentalconditions, and storing the values of COF and/or slip ratio in adatabase. Such profiles may be stored alongside descriptive informationabout the environment. The descriptive information may include time,location (e.g., as determined by GPS), other sensor information, localweather conditions, etc. The descriptive information may also includepointers to other information sets, such as weather databases, which arenot locally included in the database. Further, such profiles may beperiodically updated. This allows changing of a vehicle specific profileas conditions of the vehicle change. This may allow, for example,altering profiles as the tires of the vehicle wear.

The instantaneous ratio of the tangential velocity of the tire where itmeets the road and the velocity of the vehicle to which it is attachedis defined as the “Slip ratio”. When braking (or accelerating) in amoderate manner, the tangential velocity of the tire where it meets theroad is a little slower (or faster) than the relative velocity of thevehicle vs. the road itself, and the tire “slips”.

A vehicle safety system is most concerned with the sliding coefficientof friction, which will determine braking distance for a vehicle at agiven mass and speed. The sliding (kinetic) COF is defined asμ_(k)=F_(f)/N where F_(f) is the friction force between the vehicle andground, N is the normal force (gravity) pushing the vehicle and groundtogether, and μ_(k) is the COF. This is conceptually the sameformulation as for a block sliding along a plane, as illustrated in FIG.2. A very slick surface will have μ_(k)<<1, as the friction forces arevery low. A tire on a high quality asphalt road will have a maximum COFof about 0.85.

When a vehicle is on a flat (non-inclined) surface, the force N=mg,where m is mass and g is gravitational acceleration. In some embodimentsof the invention g=9.81 m/s, and in some embodiments of the invention gcan be modeled or derived from a look-up table based on the exactlocation of the vehicle. The force N represents the force normal to theground, and will change with the inclination of the vehicle. In someembodiments of the invention, inclination of the vehicle is measured orestimated using an inertial measurement system, which itself may includeaccelerometer(s), gyroscope(s), inclinometer(s), and/or magnetometer(s),where the information from the inertial measurement system is input intoa model to calculate inclination. In some embodiments of the invention,inclination of the vehicle is further measured using global positioningsystem (GPS) data, which can infer inclination based on known topographyof the roads, and can infer orientation based on known direction oftravel and/or based on magnetometer measurements. In one embodiment, theinertial sensors are placed on a non-rotating member of the vehicle,such as on the front bumper. In some embodiments of the invention,inclination of the vehicle is measured using a bubble sensor. In any ofthese embodiments, the inclination is used to further inform thecalculation of N and/or the calculation of COF.

In some embodiments of the invention the mass of the vehicle isestimated. In one embodiment, the mass is estimated based on a look-uptable for the vehicle or is estimated using an optical imaging sensorwhich captures the size of the tire/ground interface for one or moretires. Based on the interface information and the known tire pressure asmeasured with the tire pressure measurement system, vehicle mass iscalculated.

In some embodiments of the invention, COF is approximated using aformula that includes an input value af, defined as the differencebetween the instantaneous tangential acceleration of the tire where itmeets the road and the acceleration of the vehicle to which it isattached. Because the mass of the vehicle is a component of both thefriction force and the normal force, these cancel each other and thevalue μ_(k)=a_(f)/g is directly measurable. In one embodiment,μ_(k)=q*a_(f)/g, where q is a fitting factor which may depend on factorssuch as vehicle inclination or other sensor measurements as discussedabove.

In the above embodiments, forces are calculated using a “grey box”approach that relies on some first principles calculations. In anotherembodiment, COF itself or the component q are calculated using a “blackbox” approach that correlates COF with data derived from inertial andother sensors using a multivariable calibration fit approach withoutreliance on a specific physical model.

Measurements of slip are also useful for characterizing vehicleperformance. Such measurements are performed in the vehicle using adevice such as a hall sensor that is built into the wheel for thispurpose as part of an ABS system. In one embodiment, the inventiontracks GPS position over time to establish vehicle velocity as an inputto model slip. In this embodiment, the tangential velocity of thewheel(s) is measured using a gyro or set of gyros attached to thewheel(s), and this is input into a model for the calculation of slip. Insome embodiments, data from the accelerometer(s) is used as an input toimprove the estimation of the velocity of the vehicle and/or wheel(s),where a velocity at time t_(n) may be estimated based on knowledge ofthe velocity at a time t_(m) and the acceleration a_(n-m) during thistime.

In one embodiment, measurements of both COF and slip are made using thesame or overlapping sets of sensors. In another embodiment, slip may beinferred or calculated based on COF information. In one embodiment, datais received from inertial measurement sensors disposed on at least onerotating member, and on at least one non-rotating member. FIG. 3 showsan exemplary arrangement of sensors on a vehicle 300, with a sensor orset of sensors 301 disposed on a wheel lug nut, and a sensor or set ofsensors 302 disposed at the front bumper. FIG. 3 also shows an optionalsensor or set of sensors 303 disposed on the windshield, which can beused for at least detecting precipitation. These locations represent oneset of possible placement of sensors, and are not meant to be limiting.

The sensors are in wired or wireless communication with each otherand/or with a central communications node (device), which may be insideor outside the vehicle, and which provides communication with theoutside world. In one embodiment, communication between the sensors andthe communications node is accomplished through Bluetooth LE. In oneembodiment, a sensor may connect to a second sensor but not have adirect connection to the communications node, and in so doing, thesensors form a mesh network. Wireless communications systems may requireantennas, and antenna placement, polarization, and directionality may beimportant for the application. In one embodiment, a sensor orcommunications hub is placed inside the vehicles windshield, where thesignals satellite communications, GPS, infrastructure, and other sensorsare unimpeded, or where the signal path to other sensors has the leastobstruction (e.g., metal shielding). In another embodiment, thedirectionality, polarity, placement, and signal output timing of a wheelmounted sensor is chosen so as to improve the reception strength atanother sensor, communications hub, or device (e.g. transmission occursduring periods when the hub of the wheel is not obstructing the signalas the sensor rotates with it).

FIG. 4 shows an exemplary system 400 that includes a wheel sensor 401and a fixed sensor 402, which are in data communication with acommunications node 404. To illustrate the potential use of a meshnetwork, the communications system 400 also includes an additionalsensor(s) 403 which communicates to the fixed sensor(s) 402, but notdirectly to the communications node 404. The sensor 402 or 401 relaysdata from the sensor 403 back to the communications node using a wiredor wireless connection. This may favorably save power in someconfigurations, depending on factors such as the distance of the sensors401-403 from each other and from the communications node 404. Note thatthis configuration is not meant to be limiting, merely illustrative. Inone embodiment, a communications network might be established up fromthe individual sensors via BLE to the hub on one vehicle, via a cellnetwork from that vehicle to the cloud, then through the internet andout via wifi connections of passing homes or businesses to a secondvehicle, then between that vehicle and a third vehicle using Bluetoothor DSRC. In another embodiment, the houses or businesses might havesensor suites themselves, and pass information via wifi to the cloud, orvia wifi or Bluetooth to passing vehicles. In yet another embodiment,roadside infrastructure (e.g. stop signs or streetlights) might beoutfitted with sensors and/or communications hubs powered byphotovoltaics, and communicate to the cloud via satellite internet, andto passing vehicles using DSRC or wifi.

The communications node 404 passes sensor data to an on-board processingmodule 405 which aggregates data from each sensor 401-403. Thecommunications node 404 may also be in contact with an external networksuch as a cellular network 407, and may pass data via the cellularnetwork 407 to a cloud database 408 and a cloud computing module 409.The system of this invention may either use cloud computing module 409or the on-board processing module 405 to process data from the sensors401-403 and from the cloud components 408, 409. The on-board processingmodule 405 sends an alert to the driver via an output device(s) 406 if athreshold danger probability is reached. The output device(s) 406includes audio, visual and/or tactile systems in the vehicle. In someembodiments, the on-board processing module 405 is configured withsystem memory, which may store the one or more profiles of the vehicle.In some embodiments, this stored profile(s) includes a family of COF vsslip ratio curves. FIG. 13 shows exemplary COF/slip profile curves for avehicle for different road environmental conditions (e.g., dry pavement,wet pavement, compacted snow, smooth ice (i.e., black ice)). In someembodiments, the on-board processing module 405 is configured toretrieve profile information as an input into a model and/or for usewith one or more inputs to, for example, make predictions about vehicleperformance or estimations of environmental conditions.

In some embodiments of this configuration, the sensors 401-403 may sendraw data to the communications node 404 or to each other. In someembodiments of this configuration, the sensors 401-403 further includeonboard processing to reduce the data set and/or fuse information fromone or more sensors, and thereby reduce the total communicationsoverhead. The decision of whether to process the data on an on-boardprocessor or send raw data to the communication node 404 depends on therelative power and bandwidth requirements of each mode of operation, andmay differ for different sensors and/or locations of the sensors.Bluetooth Low Energy communication represents an exemplarycommunications operational mode, as it supports a star architecture,with the central device able to connect many peripheral devices, andsupports over-the-air updates. In one embodiment, devices coordinate to“sleep” between short transmissions, significantly reducing power use.Alternatively, information may be stored and transmitted in bursts onhigher-bandwidth, higher power-use devices such as standard Bluetooth orwifi. In that case, these devices may be powered down between bursts oftransmission to save power.

In one embodiment, communications are accomplished through means otherthan radio frequency transmission, including wired transmission, opticaltransmission, acoustic transmission, magnetic induction, or transmissionof electrical signals through the body of the vehicle, or through thevehicles on-board diagnostics (OBD), etc.

In one embodiment, one or more of the sensors 401-403, on-boardprocessing module 405, and communications systems 404 are powered byscavenged power (also known as power harvesting or energy scavenging).Energy is derived from external sources constantly during use, or isderived intermittently and stored on battery, capacitor, supercapacitor, etc. Example power sources include solar cells, kineticenergy devices that derive power from vibrational, rotational, linear,or other motion of the vehicle, or a harvesting ambient radiation sourcedevice (e.g., antenna collection of energy from radio waves, such as ina Powercast system, or via wifi or DSRC power scavenging). In oneembodiment, a radio source is provided in the vehicle to create radiowaves which are harvested by the sensors. In one embodiment, the sensorsare equipped with Piezoelectric, Pyroelectric, Thermoelectrics,Electrostatic (capacitive), Magnetic induction, Mechanical, or Microwind turbine energy harvesting capability. In one embodiment, a magneticinduction or piezo element is included in a sensor pack to harvestvibrational energy. In one embodiment, the rotation of the tire causes amagnet to move due to changing gravitational field and/or centripetalforces, inducing power in a coil for use in the system.

The system 400 may optionally include a memory device (or devices) 410that stores information (wheelbase, tire types,deceleration/acceleration capabilities, etc.) pertaining to the hostvehicle, or stores data when communications are interrupted ornon-existent The processing module 405 uses stored information togenerate profile information (described later). The memory 410 may alsostore raw and/or processed sensor information, road type/conditioninformation and weather information. The road type/condition informationand weather information as well as other information are received at thesystem 400 from an external source via the communications node 404.

FIG. 5 shows a lug nut sensor 500 that is to be attached to a vehiclewheel. In this embodiment, the lug nut sensor 500 includes a screw 501which threads through a tire package cover 502 to unite with a sensorpackage housing 506 and the lug nut 507. The housing 506 includes twolithium polymer batteries 503, an inertial measurement unit 504 havingan accelerometer and gyroscope, and a microprocessor 505 with Bluetoothcommunications capability. In one embodiment, the accelerometer includesone 6 g 3-axis accelerometer with axes pointing radially, laterally, andtangentially with regard to the tire. In one embodiment, the sensorsystem 500 includes one additional 120 g one-axis accelerometer fordirect measurement of radial accelerations at high speeds. In oneembodiment, the microprocessor 505 calibrates the sensor, samples data,and filters it to produce measurements in radial, tangential, lateralaxes of the tire. Such calibration is a typical sensor fusionapplication—e.g. gyros are prone to drift, but this can be compensatedfor in a full inertial measurement unit. Velocity measurements madeusing gyros on the tires can be undrifted GPS or accelerometers, etc.This sensor may of course alternatively be affixed to the outside of thevehicle wheel (e.g. using two-sided tape) or to the inside of thevehicle (e.g. attached to a band running along the inside of the hub).

To calibrate the tangential acceleration (y) measurement, in oneembodiment the following approach is used:

-   -   1. At constant speed and perfect alignment, tangential        acceleration (y) is 0;    -   2. Define calibrated x as positive to the “east” and calibrated        y as positive to the “north”; and    -   3. At constant speed with misalignment angle θ, defined as a        rotation of the system counterclockwise from x and y to x_(u)        and y_(u), the calibrated x and y are

x=x _(u) cos(θ)+y _(u) sin(θ)

y=y _(u) cos(θ)−x _(u) sin(θ).

In one embodiment, the effects of gravity are averaged out by takinghundreds or thousands of measurements of x_(u)/y_(u) to obtain averagemeasurements x_(u) _(_) _(avg) and y_(u) _(_) _(avg). At constantvelocity, y=0, and thus y_(u) _(_) _(avg)≅x_(u) _(_) _(avg) tan(θ), suchthat it is possible to calculate θ≅a tan(y_(u) _(_) _(avg)/x_(u) _(_)_(avg)).

In one embodiment, tangential acceleration is determined by samplingacceleration data at >30 Hz, with a predefined rate of sampling (e.g.,250 Hz). In this embodiment, over a (for example) time of measurement,the max and min values of (calibrated) y are identified, which willroughly correspond to acceleration up and acceleration down, and whichwill vary by +1 g and −1 g from true acceleration. These measurementsare averaged to cancel out the effects of gravity to obtain a tangentialacceleration estimate. This value is updated to the processor node, andthe measurement process is repeated.

In another embodiment, tangential acceleration is calculated using aKalman filter algorithm. In an exemplary process, lug nut tangentialacceleration is defined as being proportional to tire/road contact pointtangential acceleration—if R_(eff) is the effective radius of the tire(measured from tire axel to road), and R_(hub) is radius of hub to lug,then wheel_(tan) _(_) _(acc)=R_(eff)/R_(lug) lug_(tan) _(_) _(acc)+k,where k is a cyclical component due to gravity. The constants R_(eff)and R_(lug) are set using system identification or a calibration scheme.A Kalman filter which estimates the tire position and velocity—and thusthe direction of gravity—is used to filter out the cyclical accelerationcomponents due to gravity and to noise.

In another embodiment, a commercially available 6-axis sensor (x, y, andz axis accelerometer and x, y, z axis gyroscope on the same siliconchip), is used to directly measure the orientation and angular velocityof the tire. Most such chips are (presently) limited to perhaps 16 gaccelerations and 2000 degrees per second. Mounted even a fewcentimeters from the hub of the wheel (example near a lug nut), avehicle traveling at highway speeds would saturate an accelerometerchannel pointed along the radial axis, and a gyro revolving around thelateral one. This limits the ability to determine the angular velocityof the tire (and thus linear velocity of the vehicle). However, in oneembodiment the axis of measurement is offset to reduce the magnitude ofboth the acceleration and angular velocity measured. This creates a verystraightforward linear reduction in the measurement of the angularvelocity if the vehicle is going straight, but creates a complexrelationship between the angular velocities of the other axes when theauto is turning. Similarly, this can create a very straightforwardlinear reduction in the measurement radial acceleration (which can beused to estimate the angular velocity) if the vehicle is going straight,but creates a complicated relationship between the angular accelerationof the tire and the estimated angular velocity, and changes therelationship between the position components of the acceleration in theoffset radial and tangential measurements. In some embodiments, thesecomplexities are resolved through further processing in a grey box orblack box model.

FIG. 6 shows one embodiment of a wheel assembly 600 where a wheelinertial measurement sensor pack 605 is mounted at or near a tirepressure measurement sensor 601 in proximity to a valve stem 602 on awheel 604. The sensor pack 605 is connected by a valve stem retainerscrew 603, or alternatively is fixed in position by an adhesive.

In an alternate embodiment, the sensor set is affixed to the back of thewheel using an adhesive—the specific location can vary inimplementation. In some embodiments, an antenna is added to the sensorsystem in order to improve communication capability with thecommunication node. In some embodiments, the tire stem is used as theantenna.

FIG. 7 shows a suite of sensors system 700 disposed on the front bumper.The system 700 includes a bumper sensor suite housing 701, a powerswitch 702, and a charge controller and voltage regulator 703 whichcontrols charging of a battery pack 705 by a solar panel 704. The solarpanel 704 provides power to the system, and may also usefully measureinsolation power levels in real time, and therefore may also be used asa sensor. Other embodiments may utilize other power sources. The system700 further includes a Bluetooth modem 706 and a microcontroller 707,which may be housed in the same package (e.g., a system on a chip) or indifferent packages. The system 700 may optionally include an infraredthermometer 708 and/or a microphone 709, as well as an inertialmeasurement unit (IMU) 710. A cover 711 protects the components of thesystem 700.

In one embodiment, the sensors of the system 700 identify road surfacetype (e.g., concrete, asphalt, gravel, dirt), condition (e.g., worn,cracked, potholed), and covering (e.g., black ice, lose or packed snow,slush, rain, dirt, etc.). In one embodiment, the sensor(s) measureambient temperature and/or relative humidity. In one embodiment, the IRsensor(s) 708 measure temperatures in front of the front tires. Themicrophone sensor 709 measures sound that is analyzed by a processor toquantify road noise, which may be correlated to weather conditions. TheIMU 710 includes accelerometers, gyroscopes, inclinometers, and/ormagnetometers. In one embodiment, the sensors additionally includeoptical image sensor (not shown) that provides imaging data that is usedby a processor to quantify visibility, particulate counts, cloud cover,etc. In one embodiment, this status of the headlights is determinedusing sensors.

FIG. 8 shows an embodiment of a sensor that is attached to or near awindshield of the vehicle. The system 800 includes a housing 801, acharge controller and voltage regulator 802, sensor circuitry 803, asensor battery pack 804, and a solar panel 805. The solar panel 805provides power to the components, and may also usefully measureinsolation power levels in real time, and therefore may be used as asensor as well. The system 800 further includes a Bluetooth modem 807and a microcontroller 806, which may be housed in the same package(e.g., a system on a chip) or in different packages. The system 800 mayalso include a capacitive sensor 808 and/or a swept frequency sensor809, as well as an optional ambient light sensor 810. The systemcomponents are protected from the environment by a bottom level decal811. Attaching sensors inside the vehicle (e.g. inside the passengercompartment, tire, or engine housing) may serve to protect the sensorsfrom extremes of temperature, UV, humidity etc. The sensors mayalternatively/additionally be protected using superominphobic coatingsfor lenses, cases etc.

Information gathered by the system 800 includes precipitation detection,fog, rain, snow, ice, visibility and cloud cover, and/or windshieldwiper frequency. The system 800 can be mounted inside or outside ofwindshield glass. Mounting the system 800 inside will increase thepackage's life.

In one embodiment, the swept frequency sensor 809 includes a SweptFrequency Inductive Precipitation Sensor, such as that previouslydescribed in U.S. Pat. No. 6,388,453 B1. While '453 describes the use ofsine wave sweeping to obtain a response, signals besides sine waves areused—for example, a complex frequency chirp is sent, and a controlstheory/signal processing method called an empirical transfer functionestimator (ETFE) is applied to determine transfer function. Theempirical transfer function estimate is computed as the ratio of anoutput Fourier transform to an input Fourier transform, using a fastFourier transform (FFT). The periodogram is computed as the normalizedabsolute square of the Fourier transform of the time series. Smoothedversions can be obtained by applying a Hamming window to the output FFTtimes the conjugate of the input FFT, and to the absolute square of theinput FFT, respectively, and subsequently forming the ratio of theresults.

In an alternative embodiment for sensing of wiper frequency orprecipitation, a light source such as a laser is shone onto thewindshield at an angle below (or above) the Brewster's angle of theglass while dry. Precipitation causes a change in the optical indexsystem such that the light now is above (or below) the Brewster's angle.Then when the wiper blade cleans the glass, the system briefly reverts,allowing detection of both the precipitation and the wiper activationvia this optical sensor.

In one embodiment of the invention, the precipitation sensor measuresamount or rate of precipitation. In another embodiment of the invention,the precipitation sensor measures type of precipitation, for example bychanges in light scattering associated with snow. In another embodimentof the invention, precipitation type is inferred based on a combinationof sensor measurements and/or information from weather sensors externalto the vehicle.

When the vehicle is operating, both slip and COF are calculatedcontinuously as the vehicle runs based on a data set including at leastdata from the wheel-mounted IMU and the fixed IMU. If the environmentwere always constant, this information could be used to define a curveshowing the relationship between COF and slip. However, because roadconditions change as the vehicle moves, there is no single curvedefining the performance of the vehicle, and a profile of curves isbuilt.

In one embodiment, vehicle environmental conditions are separated byseparating COF vs slip ratio data into different clusters ofperformance, using a technique such as K-means.

In a further embodiment, this data on COF vs slip ratio is added to adatabase alongside further information including time, location,traffic, road type, and/or environmental conditions local to the datacapture event. Road conditions are quantified based on an estimated riskassociated with the known road type, for example scoring 1=dirt road,5=highway, etc. In one embodiment, road conditions are quantified basedon a score derived from COF measurements made by multiple vehicles.Environmental conditions are quantified on one or more axis to enhancemathematical processing of the data. For example, environmentalconditions may be scored in terms of ambient temperature (for example,in ° C.), road temperature (for example, in ° C.), insolation power (forexample, in W/m²), precipitation intensity (for example, in cm/hr), etc.In some embodiments, an aggregate environmental score is compiled basedon the hazard implied by different environmental elements. In oneembodiment, an aggregate environment score is compiled with informationabout known road type and known environmental conditions. For example, abridge may receive a high composite score under warm, sunny conditions,but may receive a dramatically lower score under cold, snowy conditions.

Elements of the database which are measured in high confidence may beusefully employed to identify more accurate values for elements of thedatabase which have lower confidence. In one embodiment, a measured COFor slip value may be used to estimate an environmental condition, or aknown environmental condition may be used to estimate a COF or slipvalue.

In one embodiment, measurements of COF and slip in known goodenvironmental conditions (e.g., warm and sunny) is combined to create acurve for the vehicle that is generally accurate for good environmentalconditions, thus eliminating the previously stated difficulty ofclustering data automatically. FIG. 9 shows a system that builds aprofile, where information from wheel inertial sensors 901, the fixedinertial sensor 902, and optionally the GPS 903 are transferred to aCOF/slip computational module 904, which calculates the local COF andslip ratio associated with this set of sensor data. This information istransferred to a vehicle profile calculator 906, which fuses the COF andslip ratio information with information from an environmental database905 and/or GPS data to create a profile for the vehicle. Thisinformation may optionally be transferred to an on-board vehicle profiledatabase 907 and/or a vehicle profile database 908 in the cloud.

In another embodiment, measurements of COF for a vehicle is used tosuccessfully identify adverse environmental conditions such as blackice. In this way, hyper-local environmental changes such as icing areeasily identified by examining the COF performance of the vehicle aftera profile has been created. FIG. 10 shows a system that estimatesenvironmental conditions, where information from wheel inertial sensors1001, the fixed inertial sensor 1002, and optionally a GPS 1003 aretransferred to a COF/slip computational module 1004, which calculatesthe local COF and slip associated with this set of sensor data. Thisinformation is transferred to an environmental conditions computationmodule 1005, which fuses the COF and slip information with informationfrom the vehicle profile 1006 and/or GPS data to estimate environmentalconditions for the vehicle. This information may optionally betransferred to an environmental profile database 908 in the cloud, whereit may be usefully applied to warn other drivers of adverse weatherconditions in the GPS location where the measurement was taken.

Such a warning system is described in FIG. 11. In this embodiment,information on the vehicle location from a GPS 1102 and optionally froma trip path module 1101 is fed into a vehicle location prediction module1103, which predicts the future locations of the vehicles during thetrip. This information is fed into an environmental prediction model1105 alongside environmental profile information from a database 1104from the cloud. The environmental profile information includes data fromthe national weather service, local sensors, and/or data collected byother vehicles using the system described in FIG. 10 above, as well asother mobile vehicle weather collection processes. The environmentalprediction information is passed to a vehicle warning module 1107, whichcompares the environmental prediction with the vehicle COF/slip profileto identify whether the predicted environment will be outside thesuggested operating specification for a vehicle with that profile. If athreshold is passed, this information is sent as a warning to aninterested party 1108. An interested party includes a driver, or a fleetowner, or an insurance operator, etc.

Information, guidance, and warnings may be provided in many ways,including via smart phone or watch (e.g. alarm bells, vibration, texts,phone calls, via traffic apps, text to speech, satellite communicationssystem such as OnStar, and visual cues), as well as visually orauditorially through the vehicle's OBD display, navigation display ortext to speech system, radio/entertainment console, vehicle oraftermarket heads up display, DSRC warning system, and many other means.In one embodiment, the information, guidance, and warnings are deliveredvia text to speech or heads up display to preserve driver concentrationthe road, In another embodiment, the warnings are integrated with thevehicles safety system to take action if the driver does not. In anotherembodiment, the information, guidance and warnings are delivered to aself-driving vehicle, so that the vehicle or driver may take appropriateaction. In another embodiment, the guidance takes the form of a safe oradvised driving speed, or warning to slow down. In another embodiment,the driver or navigator uses voice commands to request information,guidance, or warnings. In another embodiment, the warnings take the formof an escalating series of warnings with regard to weather, safe drivingspeed, safe stopping distance, or road conditions.

One novel thing element is that, before the weather moved and the (oftensparsely located) sensors stayed still, the presented system uses movingmobile sensors that can send information machine-to-machine (M2M). Thecombination of mobility and M2M creates a “crowd-sourced” mobile sensor“fabric”, and the fabric is constructed such that most information isboth generated and consumed where there are the most users and sensors.Individual people may perceive changing clouds and precipitation in onearea, but networked and fused sensors see changes in pressure,irradiance, humidity, and precipitation rates over large areas, as wellas have access to historical weather and data patterns, and thus thewhole system is able to do analysis and prediction different in kindrather than degree.

In one embodiment, a plurality of sensors send to a smartphone acting asa hub, which aggregates, organizes, fuses and/or prepares information; aplurality of smartphones, posts that information to a collection system,where it is quality control checked; a buffering system stores andprioritizes and organizes the data in queues; the data are then fusedwith existing data such as weather, road, GIS, databases, to create acurrent situational picture; these situational pictures are madeavailable using (for example) geofencing techniques, both to alert andorganize data about motorists and geographic areas; geofencing impliesthat now we can follow up with a set of triggers, these triggers beingassigned to mobile entities, based on fused criteria, indicating desiredalerts based on individual preferences.

Additionally, such a machine-to-machine system is able to returninformation back quickly and per user preferences—the system can have“smart triggers”. A simple temperature gauge may alert a user with a redlight when the temperature goes below a set value, but a smart triggerseeks information and makes warnings that are context sensitive—such aswarning as user about how the confluence of the rate of decrease inpavement temperature and predicted precipitation may generate frozenpavement. Using modern software technologies like Pagerank or Twitterthat look for important signals using eigenvectors, these signals canresult in information, guidance, or warnings routed to a unique user byindexes to the most important links in the eigenvector in a very fastmanner. In such a system, metadata is fused together, an eigenvectoranalysis is run, then indexed the most important events, making itlightning fast to both find the smart triggers and/or users. This canprovide fast M to M alerts—in one embodiment, machines automaticallyspraying salt on a road that will soon require it, or lower barricadeson roads that may soon experience white out conditions, or trigger roadsignals warning of black ice, all in a very fast and automated manner,scalable to huge numbers of users and triggers.

Use cases for the user criteria of such a system include: soccer mom'scriteria is whether she can drive 3 miles safely in the small geofencedarea between home and practice; a medium-haul limo service will look ata larger geofenced area, and want fused information about traffic,weather, known pick up sites, and historical patterns in order to makethe most efficient run; maintenance and logistics organizations willwant to watch vehicles roll over road segments to see what needs repairor where slowdown may be predicted to occur—their user criteria may bereal-time analysis, or it may be a forecast about the desirability ofsalting an iced road in the next four hours, paving a bumpy road in thenext four months, or allocating a budget for the next four years; longhall trucking businesses may wish to add weather and road conditionforecasts to the fleet management and fleet routing services that arecommonly employed by such concerns.

The availability of the various information available to such a systemmay be used in novel ways. For instance, unique signals can be createdwhich may be analyzed using advanced mathematical and analyticaltechniques, identifying conditions, and forecasting conditions in waysthat were previously unavailable (e.g., machine learning algorithms withnovel features for weather knowledge and actionable information, andneural networks provide logistic regression outputs not previouslyavailable do to the scarcity of information about road weatherconditions).

A method of using this data may for example include receiving tire slipinformation and/or COF information from vehicle sensors; receiving oneor more external environmental conditions information from a database;receiving a route request having at least route information and time ofdeparture information; generating safety values for a plurality ofportions of the requested route based on the received environmentalconditions information and previously stored vehicle performanceinformation associated with the route request; determining if thegenerated safety values meet at least one of a predefined safetythreshold or a time of travel threshold; if the determination indicatesthat one of the safety values fails to meet the at least one safetythreshold or the time of travel threshold, generating at least one of anew route or a new time of departure that would cause the generatedsafety values to meet the safety threshold or the time of travelthreshold; and presenting the generated new route or new time ofdeparture to a user or interested party associated with the routerequest. In one embodiment, the database data includes COF/slipinformation collected from a plurality of sensors located on a pluralityof ground vehicles.

As noted, vehicle sensor and/or profile information may be usefullycombined into a vehicle library or database in the cloud. This libraryor database will allow estimation of COF/slip performance in weatherand/or road conditions for a specified vehicle, even if that vehicledoes not have a profile that extends to the current environmental and/orconditions, by comparing this specified vehicle with other vehicles withsimilar properties and/or using sensor outputs from other vehicles.Similar properties may include, but are not limited to, similarmodel/make, similar tires, similar number of miles on the tires, similarprofiles in measured weather conditions, COF measurements of vehiclestraveling over current trip path of a vehicle, etc.

Such a process is shown in FIG. 12, where sensor measurements and/orprofiles from multiple vehicles 1201, 1202, 1203, etc. are combined intoa library 1204. In such an arrangement, a vehicle with an incompleteprofile 1205 does not necessarily have measured data that correlates tothe specific weather conditions. As a result, its performance can beestimated by the vehicle prediction module 1206 by extrapolating fromdata for similar vehicles profiles in the library.

In one embodiment, a vehicle may receive information from the cloudbased database (or other wirelessly accessible database) for use withvehicle profile information. For instance, FIG. 14 illustrates anexpected travel path of a vehicle traveling between first and secondlocations (e.g., Idaho Springs, Colo. and Silverthorne, Colo.). Such anexpected travel path may be inferred based on a current travel directionof a vehicle, previous user information, or entered by a user. Thedatabase may provide information for the expected travel path to thevehicle. In this regard, the database may include measurements and/orprofiles of vehicles having previously traveled over the expected travelpath. Such information may be for vehicles that have traveled over theexpected travel path within a predetermined time period (e.g., previousfifteen minutes, hour, six hours, day etc.) In the present embodiment,the database may provide prior COF information/measurements of vehiclespassing over the travel path. In this regard, prior COF information 1402may be provided for predetermined road segments (e.g., every quartermile) and/or for changes in road geography, surface and/or roadstructure (e.g., changes in road grade, changes from asphalt toconcrete, changes from new asphalt to worn asphalt, bridge susceptibleto icing, etc.). This is illustrated on the map shown in FIG. 14 whichshows prior COF information 1402 that is provided for different segmentsof the travel path.

The prior COF information for the travel path may be determined in anymanner from previously reported COF information. For instance, the priorCOF information may be an average of all COFs reported by vehicleshaving previously passed over all or portions of the travel path. Anyother mathematical representation (e.g., mode, mean etc.) of the priorCOFs may be provided. The prior COF information may be further analyzedbased on, for example vehicle type. In this regard, the type of vehicleon the travel path may be known and the vehicle may request or otherwisereceive COF information for like vehicles: rear wheel drive vehicles,small all wheel drive, large all wheel drive, trucks, etc. That is,prior COF information for like vehicles may be provided along the travelpath.

Upon receiving prior COF information, the vehicle may correlate theprior COF information for upcoming segments with the profiles 1302 a-nstored in the on-board vehicle profile database 907. Alternatively, thevehicle may access stored profiles from the cloud based database 908.See FIG. 9. The cloud based profiles may be generated by the subjectvehicle or may be profiles of other like vehicles. In any arrangement,the vehicle profile computation module 906 may utilize the prior COFinformation with the profiles 1302 a-n to determine the expectedperformance of the vehicle on the upcoming road segment. For instance,an expected wheel slip percentage may be calculated.

As shown in FIG. 13, using the prior COF information as an input withthe profiles 1302 allows for determining an expected slip percentage ifthe environmental conditions of the road segment is known ordeterminable. Such environmental conditions may be determined usingsensors of the vehicle. Alternatively, prior environmental conditions1404 may be provided to the vehicle with the prior COF information. SeeFIG. 14. Stated otherwise, the library may, in addition to providingprior COF information, provide prior environmental information 1404 asreported by previous vehicles passing over the expected travel path.More generally, the library may provide road surface information (e.g.,COF information and environmental information) to the vehicle. In eithercase, the vehicle traveling on the travel path may utilize the COFinformation and/or environmental information with stored profiles (SeeFIG. 13) to determine performance/safety information for the vehicleprior to the vehicle passing over upcoming segments of the travel path.

Based on the estimated wheel slip of the vehicle, various outputs (e.g.,predictions) may be provided to the driver of the vehicle and/or to thecontrol systems of the vehicle. For instance, if a slip percentage foran upcoming road segment exceeds a predetermined threshold, a warningoutput may be generated. In a further arrangement, an alternate route1502 may be suggested if a slip percentage for an upcoming road segmentexceeds a predetermined threshold. See FIG. 15.

FIG. 16 illustrates a process 1600 for utilizing prior road surfaceinformation at a vehicle. The process begins with the establishing 1602of a wireless connection between a vehicle and a road surface database.Once communications exist between the vehicle and the database, thevehicle may request and/or receive 1604 road surface information fromthe database for a travel path of the vehicle. In some instances, thedatabase may be operative to push data to the vehicle without a requestoriginating from the vehicle. That is, if conditions warrant providingdata, the database may initiate contact and/or automatically providedata to a vehicle. The road surface information typically includes COFinformation for one or more segments of the travel path. The roadsurface information may further include environmental information forthe one or more segments of the travel path. An on-board processor ofthe vehicle then accesses 1606 one or more profiles of the vehicle. Suchaccess may be from local storage or via the wireless connection. Usingthe road surface information and the profile(s), the processor isoperative to calculate 1608 estimated wheel slip for one or moreupcoming segments of the travel path. If one or more of the wheel slipestimates exceed a predetermined threshold(s), an output may begenerated 1610 for receipt by the driver of the vehicle and/or vehiclecontrol systems. Such driver outputs may be related to speed reductionrecommendations and alternate route suggestions among others.

FIG. 17 illustrates a process 1700 for gathering and distributing roadsurface information. Initially, a processing platform/database receives1702 road surface reports from a plurality of vehicles traveling overroads. These road surface reports typically include COF informationdetermined by the vehicles along with location information identifyingwhere the COF information was determined. The road surface reports mayalso include environmental information measured directly or from whichenvironmental information for the location may be determined (e.g., inconjunction with a weather model). The processing platform processes andstores 1704 information from or derived from the road surface reports.At a subsequent time, a request for road surface information for atravel path is received 1706 from a vehicle or the processing platformdetermines a vehicle is traveling a travel path for which pertinent roadsurface information is available. In the latter regard, the processingplatform may be receiving road surface reports from a vehicle and if noadverse road conditions are known, not information may be provided.Conversely, if upcoming road conditions are determined adverse (e.g.,COF for a road segment drops below a predetermined threshold)information may be pushed to the vehicle absent a request from thevehicle. In any case, stored road surface information is then processedto identify 1708 prior road surface information for the travel path. Theidentified road surface information is then sent 1710 to the requestingvehicle.

The modules 904, 906, 1004, 1005, 1101, 1103, 1105, 1107, and 1206and/or processes described in relation to FIGS. 9-12 and 16-17 areprocessing functions that may be performed by processors located at oneor more of the locations such as the lug nut, bumper or windshieldsystems, or at a processor located on-board or off-board the vehicle.

The foregoing description of the present invention has been presentedfor purposes of illustration and description. Furthermore, thedescription is not intended to limit the invention to the form disclosedherein. Consequently, variations and modifications commensurate with theabove teachings, and the skill or knowledge of the relevant art can bemade within the scope of the present invention. The embodimentsdescribed hereinabove are further intended to explain best modes knownfor practicing the invention and to enable others skilled in the art toutilize the invention in such, or other, embodiments and with variousmodifications required by the particular applications or uses of thepresent invention. It is intended that the appended claims be construedto include alternative embodiments to the extent permitted by the priorart.

1. A method for use in predicting vehicle performance over a roadsegment, comprising: establishing a wireless connection between aprocessing unit of a vehicle and a database having road surfaceinformation for a travel path of the vehicle, wherein said road surfaceinformation is compiled from vehicles previously passing over at least aportion of said travel path; receiving, at the processing unit, roadsurface information for upcoming road segments along said travel path;accessing stored profile information for the vehicle, wherein saidprofile information includes wheel slip information indexed to at leasta portion of said road surface information; calculating an estimatedwheel slip of said vehicle for at least one upcoming road segment alongsaid travel path using said stored profile information and said roadsurface information; and upon said estimated wheel slip exceeding apredetermined threshold, generating a safety output.